Detection of Snow Cover from Historical and Recent AVHHR Data—A Thematic TIMELINE Processor

Roessler, Sebastian; Dietz, A.J.

doi:10.3390/geomatics2010009

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…For example, the Global Snow Pack (GSP) developed by Dietz et al [19] is a global MODIS-based daily snow cover product enabling snow cover analyses at a 500 m resolution starting in the year 2000. Even longer time series of snow cover dynamics have been generated on the basis of data from the Advanced Very High-Resolution Radiometer (AVHRR) [20][21][22][23]. Both sensors offer very high temporal resolutions, which makes them particularly suitable for the analysis of snow-related climatology and phenology.…”

Section: Introductionmentioning

confidence: 99%

Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series

et al. 2022

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Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

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Section: Introductionmentioning

confidence: 99%

Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series

et al. 2022

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show abstract

“…For example, the Global Snow Pack (GSP) developed by Dietz et al [18] is a global MODIS-based daily snow cover product enabling snow cover analyses at a 500 m resolution starting in the year 2000. Even longer time series of snow cover dynamics have been generated on the basis of data from the Advanced Very High Resolution Radiometer (AVHRR) [19][20][21][22]. Both sensors offer very high temporal resolutions, which makes them particularly suitable for the analysis of snow-related climatology and phenology.…”

Section: Introductionmentioning

confidence: 99%

Towards forecasting future Snow Cover Dynamics in the European Alps – The Potential of Long Optical Remote-Sensing Time Series

Koehler¹,

Bauer²,

Dietz³

et al. 2022

Preprint

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Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

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A European Time Series of Daily Snow Cover From Avhrr Data Over 36 Years: Results of the Timeline Project

Roessler,

Dietz,

Holzwarth

2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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The TIMELINE project is dedicated to generating comprehensive and consistent long-term time series using NOAA and METOP AVHRR data for Europe and North Africa, utilizing the extensive DLR-DFD historical data archive. Its primary objective is to address socially significant inquiries regarding climate change and identify potential trends. To achieve this, the project has developed a multitude of processors that produce essential data products pertaining to radiation, land surface, atmosphere, and cryosphere. Specifically focusing on the cryosphere, the daily estimation of the snow-covered area from AVHRR data between 1982 and 2018 in TIME-LINE provides an unprecedented 36-year period of observation, almost doubling the duration of available MODIS snow product records in the region. This paper presents the snow cover processor, along with the initial outcomes of the time series analysis, shedding light on important insights gained from the data.

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Detection of Snow Cover from Historical and Recent AVHHR Data—A Thematic TIMELINE Processor

Cited by 4 publications

References 42 publications

Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series

Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series

Towards forecasting future Snow Cover Dynamics in the European Alps – The Potential of Long Optical Remote-Sensing Time Series

A European Time Series of Daily Snow Cover From Avhrr Data Over 36 Years: Results of the Timeline Project

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